Overview

Dataset statistics

Number of variables25
Number of observations3953
Missing cells1047
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory772.2 KiB
Average record size in memory200.0 B

Variable types

CAT14
NUM10
BOOL1

Warnings

Name has a high cardinality: 3682 distinct values High cardinality
Email ID has a high cardinality: 3373 distinct values High cardinality
University has a high cardinality: 3140 distinct values High cardinality
Zip Code has a high cardinality: 615 distinct values High cardinality
Funded amnt inv is highly correlated with Loan Amnt and 1 other fieldsHigh correlation
Loan Amnt is highly correlated with Funded amnt inv and 1 other fieldsHigh correlation
INSTALLMENT is highly correlated with Loan Amnt and 1 other fieldsHigh correlation
Sub Grade is highly correlated with GRADEHigh correlation
GRADE is highly correlated with Sub GradeHigh correlation
Name has 271 (6.9%) missing values Missing
Email ID has 580 (14.7%) missing values Missing
Gender has 78 (2.0%) missing values Missing
University has 118 (3.0%) missing values Missing
Name is uniformly distributed Uniform
Email ID is uniformly distributed Uniform
University is uniformly distributed Uniform
Dt_Applied has unique values Unique
Delinq 2Yrs has 3628 (91.8%) zeros Zeros
Inq Last 6Mths has 1822 (46.1%) zeros Zeros
Revol Bal has 42 (1.1%) zeros Zeros

Reproduction

Analysis started2020-11-20 06:59:08.135726
Analysis finished2020-11-20 06:59:31.791276
Duration23.66 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3682
Distinct (%)100.0%
Missing271
Missing (%)6.9%
Memory size30.9 KiB
Emily Rivallant
 
1
Edlin Firmin
 
1
Sammy Becken
 
1
Upton Iacovides
 
1
Vinny Richardes
 
1
Other values (3677)
3677 
ValueCountFrequency (%) 
Emily Rivallant1< 0.1%
 
Edlin Firmin1< 0.1%
 
Sammy Becken1< 0.1%
 
Upton Iacovides1< 0.1%
 
Vinny Richardes1< 0.1%
 
Roarke Purry1< 0.1%
 
Walton Grishanin1< 0.1%
 
Gallagher Lowerson1< 0.1%
 
Paola Angelini1< 0.1%
 
Nicky Sinclaire1< 0.1%
 
Other values (3672)367292.9%
 
(Missing)2716.9%
 
2020-11-20T00:59:31.966084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3682 ?
Unique (%)100.0%
2020-11-20T00:59:32.138414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length14
Mean length13.27649886
Min length3

Email ID
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3373
Distinct (%)100.0%
Missing580
Missing (%)14.7%
Memory size30.9 KiB
rcripin60@a8.net
 
1
tsaterthwaitd5@prnewswire.com
 
1
olabrohu@elpais.com
 
1
tbossingham1n@yahoo.com
 
1
jdeenerb@barnesandnoble.com
 
1
Other values (3368)
3368 
ValueCountFrequency (%) 
rcripin60@a8.net1< 0.1%
 
tsaterthwaitd5@prnewswire.com1< 0.1%
 
olabrohu@elpais.com1< 0.1%
 
tbossingham1n@yahoo.com1< 0.1%
 
jdeenerb@barnesandnoble.com1< 0.1%
 
bstobiepf@infoseek.co.jp1< 0.1%
 
omcowen5u@tripod.com1< 0.1%
 
swiszniewskint@hexun.com1< 0.1%
 
callridgeqk@friendfeed.com1< 0.1%
 
sgueroladd@cbc.ca1< 0.1%
 
Other values (3363)336385.1%
 
(Missing)58014.7%
 
2020-11-20T00:59:32.325610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3373 ?
Unique (%)100.0%
2020-11-20T00:59:32.496069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length21
Mean length19.06982039
Min length3

Gender
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing78
Missing (%)2.0%
Memory size30.9 KiB
Male
1970 
Female
1905 
ValueCountFrequency (%) 
Male197049.8%
 
Female190548.2%
 
(Missing)782.0%
 
2020-11-20T00:59:32.640608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:32.726341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:32.827507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.944093094
Min length3

Dt_Applied
Categorical

UNIQUE

Distinct3953
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
10/03/89
 
1
08/07/81
 
1
13/08/88
 
1
05/12/83
 
1
05/09/85
 
1
Other values (3948)
3948 
ValueCountFrequency (%) 
10/03/891< 0.1%
 
08/07/811< 0.1%
 
13/08/881< 0.1%
 
05/12/831< 0.1%
 
05/09/851< 0.1%
 
12/02/911< 0.1%
 
12/03/871< 0.1%
 
02/06/821< 0.1%
 
27/12/821< 0.1%
 
07/06/821< 0.1%
 
Other values (3943)394399.7%
 
2020-11-20T00:59:33.004296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3953 ?
Unique (%)100.0%
2020-11-20T00:59:33.160151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

University
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3140
Distinct (%)81.9%
Missing118
Missing (%)3.0%
Memory size30.9 KiB
Tampere Polytechnic
 
4
Universidad Tecnológica de México
 
4
Jiangxi University of Traditional Chinese Medicine
 
4
Fukuoka Institute of Technology
 
4
Universidad Valle del Momboy
 
4
Other values (3135)
3815 
ValueCountFrequency (%) 
Tampere Polytechnic40.1%
 
Universidad Tecnológica de México40.1%
 
Jiangxi University of Traditional Chinese Medicine40.1%
 
Fukuoka Institute of Technology40.1%
 
Universidad Valle del Momboy40.1%
 
Christchurch Polytechnic Institute of Technology40.1%
 
Carlow College40.1%
 
Arab Open University40.1%
 
Abant Izzet Baysal University40.1%
 
Phillips Graduate Institute40.1%
 
Other values (3130)379596.0%
 
(Missing)1183.0%
 
2020-11-20T00:59:33.328620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2542 ?
Unique (%)66.3%
2020-11-20T00:59:33.510989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length114
Median length28
Mean length29.67088287
Min length3

Loan Amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct434
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13017.49937
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-20T00:59:33.676155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q16500
median12000
Q317625
95-th percentile30000
Maximum35000
Range34000
Interquartile range (IQR)11125

Descriptive statistics

Standard deviation8155.330342
Coefficient of variation (CV)0.6264897821
Kurtosis0.3258532123
Mean13017.49937
Median Absolute Deviation (MAD)5500
Skewness0.9233128761
Sum51458175
Variance66509412.98
MonotocityNot monotonic
2020-11-20T00:59:33.841019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120003158.0%
 
100002596.6%
 
150001904.8%
 
200001744.4%
 
60001654.2%
 
50001533.9%
 
350001433.6%
 
80001243.1%
 
16000992.5%
 
25000972.5%
 
Other values (424)223456.5%
 
ValueCountFrequency (%) 
1000210.5%
 
11001< 0.1%
 
120090.2%
 
130020.1%
 
13251< 0.1%
 
ValueCountFrequency (%) 
350001433.6%
 
344751< 0.1%
 
3400020.1%
 
339501< 0.1%
 
3360020.1%
 

Funded amnt inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct828
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12809.79216
Minimum750
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-20T00:59:34.014145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum750
5-th percentile3000
Q16500
median11775
Q317000
95-th percentile29735
Maximum35000
Range34250
Interquartile range (IQR)10500

Descriptive statistics

Standard deviation7935.907682
Coefficient of variation (CV)0.619518848
Kurtosis0.3951370723
Mean12809.79216
Median Absolute Deviation (MAD)5275
Skewness0.9263171893
Sum50637108.41
Variance62978630.74
MonotocityNot monotonic
2020-11-20T00:59:34.179554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120002496.3%
 
100002225.6%
 
60001533.9%
 
50001433.6%
 
150001393.5%
 
80001132.9%
 
7000872.2%
 
3000741.9%
 
20000721.8%
 
14000641.6%
 
Other values (818)263766.7%
 
ValueCountFrequency (%) 
7501< 0.1%
 
1000200.5%
 
11001< 0.1%
 
120090.2%
 
130020.1%
 
ValueCountFrequency (%) 
35000370.9%
 
34997.352451< 0.1%
 
34993.655391< 0.1%
 
34987.984521< 0.1%
 
34987.271011< 0.1%
 

TERM
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
36 months
2687 
60 months
1266 
ValueCountFrequency (%) 
36 months268768.0%
 
60 months126632.0%
 
2020-11-20T00:59:34.350068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:34.438951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:34.534334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Int Rate
Real number (ℝ≥0)

Distinct35
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1296908677
Minimum0.06
Maximum0.241
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-20T00:59:34.665982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.066
Q10.099
median0.127
Q30.16
95-th percentile0.203
Maximum0.241
Range0.181
Interquartile range (IQR)0.061

Descriptive statistics

Standard deviation0.04160931484
Coefficient of variation (CV)0.3208345782
Kurtosis-0.6951924625
Mean0.1296908677
Median Absolute Deviation (MAD)0.033
Skewness0.226416223
Sum512.668
Variance0.001731335081
MonotocityNot monotonic
2020-11-20T00:59:34.811507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
0.1173248.2%
 
0.1272596.6%
 
0.0792596.6%
 
0.1242546.4%
 
0.1352315.8%
 
0.1432265.7%
 
0.1072135.4%
 
0.0992115.3%
 
0.0891985.0%
 
0.061604.0%
 
Other values (25)161840.9%
 
ValueCountFrequency (%) 
0.061604.0%
 
0.0661563.9%
 
0.0751373.5%
 
0.0792596.6%
 
0.0891985.0%
 
ValueCountFrequency (%) 
0.24120.1%
 
0.23960.2%
 
0.23560.2%
 
0.23140.1%
 
0.22760.2%
 

INSTALLMENT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1923
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.2073362
Minimum32.23
Maximum1283.5
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-20T00:59:34.971897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum32.23
5-th percentile93.88
Q1205.86
median336
Q3494.59
95-th percentile813.626
Maximum1283.5
Range1251.27
Interquartile range (IQR)288.73

Descriptive statistics

Standard deviation220.261152
Coefficient of variation (CV)0.5870385006
Kurtosis0.8900854243
Mean375.2073362
Median Absolute Deviation (MAD)140.06
Skewness0.9837168213
Sum1483194.6
Variance48514.9751
MonotocityNot monotonic
2020-11-20T00:59:35.144490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
330.76270.7%
 
396.92250.6%
 
325.74220.6%
 
386.7210.5%
 
339.31200.5%
 
322.25190.5%
 
334.16190.5%
 
343.09180.5%
 
190.52180.5%
 
368.45170.4%
 
Other values (1913)374794.8%
 
ValueCountFrequency (%) 
32.231< 0.1%
 
32.5820.1%
 
33.0820.1%
 
33.551< 0.1%
 
33.9430.1%
 
ValueCountFrequency (%) 
1283.51< 0.1%
 
1276.61< 0.1%
 
1269.731< 0.1%
 
1243.851< 0.1%
 
1222.031< 0.1%
 

GRADE
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
B
1262 
A
908 
C
811 
D
510 
E
313 
Other values (2)
149 
ValueCountFrequency (%) 
B126231.9%
 
A90823.0%
 
C81120.5%
 
D51012.9%
 
E3137.9%
 
F1253.2%
 
G240.6%
 
2020-11-20T00:59:35.309556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:35.409431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:35.549315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Sub Grade
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
B3
324 
B5
 
260
A4
 
259
B4
 
254
C1
 
231
Other values (30)
2625 
ValueCountFrequency (%) 
B33248.2%
 
B52606.6%
 
A42596.6%
 
B42546.4%
 
C12315.8%
 
C22275.7%
 
B22135.4%
 
B12115.3%
 
A51985.0%
 
A11584.0%
 
Other values (25)161840.9%
 
2020-11-20T00:59:35.700228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:35.845066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Home Ownership
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
RENT
2081 
MORTGAGE
1577 
OWN
295 
ValueCountFrequency (%) 
RENT208152.6%
 
MORTGAGE157739.9%
 
OWN2957.5%
 
2020-11-20T00:59:35.991008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:36.087168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:36.462542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length5.521123198
Min length3

Annual Inc
Real number (ℝ≥0)

Distinct813
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66175.97354
Minimum8280
Maximum550000
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-20T00:59:36.600184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8280
5-th percentile25000
Q140100
median57000
Q380000
95-th percentile135880
Maximum550000
Range541720
Interquartile range (IQR)39900

Descriptive statistics

Standard deviation40498.80417
Coefficient of variation (CV)0.6119865264
Kurtosis18.71426089
Mean66175.97354
Median Absolute Deviation (MAD)18000
Skewness3.058200935
Sum261593623.4
Variance1640153139
MonotocityNot monotonic
2020-11-20T00:59:36.779051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
600001543.9%
 
500001493.8%
 
750001203.0%
 
400001203.0%
 
450001142.9%
 
70000962.4%
 
80000932.4%
 
30000932.4%
 
65000882.2%
 
35000822.1%
 
Other values (803)284471.9%
 
ValueCountFrequency (%) 
82801< 0.1%
 
84001< 0.1%
 
96001< 0.1%
 
99601< 0.1%
 
100001< 0.1%
 
ValueCountFrequency (%) 
5500001< 0.1%
 
5250001< 0.1%
 
4080001< 0.1%
 
40000020.1%
 
3650001< 0.1%
 
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
Verified
1515 
Not Verified
1247 
Source Verified
1191 
ValueCountFrequency (%) 
Verified151538.3%
 
Not Verified124731.5%
 
Source Verified119130.1%
 
2020-11-20T00:59:36.948173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:37.045955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:37.165230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length12
Mean length11.37085758
Min length8
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
0
3275 
1
678 
ValueCountFrequency (%) 
0327582.8%
 
167817.2%
 
2020-11-20T00:59:37.261907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

PURPOSE
Categorical

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
debt_consolidation
2102 
credit_card
792 
other
297 
home_improvement
 
196
small_business
 
145
Other values (8)
421 
ValueCountFrequency (%) 
debt_consolidation210253.2%
 
credit_card79220.0%
 
other2977.5%
 
home_improvement1965.0%
 
small_business1453.7%
 
major_purchase1002.5%
 
car902.3%
 
wedding631.6%
 
medical521.3%
 
moving391.0%
 
Other values (3)771.9%
 
2020-11-20T00:59:37.357393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:37.498003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length18
Mean length14.28307614
Min length3

Zip Code
Categorical

HIGH CARDINALITY

Distinct615
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
900xx
 
55
606xx
 
55
100xx
 
54
112xx
 
50
945xx
 
49
Other values (610)
3690 
ValueCountFrequency (%) 
900xx551.4%
 
606xx551.4%
 
100xx541.4%
 
112xx501.3%
 
945xx491.2%
 
070xx451.1%
 
331xx441.1%
 
750xx411.0%
 
300xx411.0%
 
113xx401.0%
 
Other values (605)347988.0%
 
2020-11-20T00:59:37.674503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique148 ?
Unique (%)3.7%
2020-11-20T00:59:37.827904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

Add State
Categorical

Distinct43
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
CA
729 
NY
372 
FL
304 
TX
273 
NJ
 
181
Other values (38)
2094 
ValueCountFrequency (%) 
CA72918.4%
 
NY3729.4%
 
FL3047.7%
 
TX2736.9%
 
NJ1814.6%
 
IL1553.9%
 
GA1463.7%
 
PA1363.4%
 
VA1303.3%
 
OH1243.1%
 
Other values (33)140335.5%
 
2020-11-20T00:59:37.992702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:38.150099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

DTI
Real number (ℝ≥0)

Distinct1961
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.42828738
Minimum0
Maximum29.85
Zeros3
Zeros (%)0.1%
Memory size30.9 KiB
2020-11-20T00:59:38.296865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.932
Q19.58
median14.45
Q319.47
95-th percentile24.214
Maximum29.85
Range29.85
Interquartile range (IQR)9.89

Descriptive statistics

Standard deviation6.378445753
Coefficient of variation (CV)0.4420792008
Kurtosis-0.7703420751
Mean14.42828738
Median Absolute Deviation (MAD)4.94
Skewness-0.04903565752
Sum57035.02
Variance40.68457022
MonotocityNot monotonic
2020-11-20T00:59:38.464611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.890.2%
 
18.6380.2%
 
20.8880.2%
 
9.6570.2%
 
12.4870.2%
 
18.8470.2%
 
17.6770.2%
 
16.470.2%
 
19.6370.2%
 
16.270.2%
 
Other values (1951)387998.1%
 
ValueCountFrequency (%) 
030.1%
 
0.0220.1%
 
0.071< 0.1%
 
0.21< 0.1%
 
0.251< 0.1%
 
ValueCountFrequency (%) 
29.851< 0.1%
 
29.831< 0.1%
 
29.731< 0.1%
 
29.721< 0.1%
 
29.631< 0.1%
 

Delinq 2Yrs
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1085251708
Minimum0
Maximum6
Zeros3628
Zeros (%)91.8%
Memory size30.9 KiB
2020-11-20T00:59:38.607774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4087983222
Coefficient of variation (CV)3.766852606
Kurtosis32.99870086
Mean0.1085251708
Median Absolute Deviation (MAD)0
Skewness4.954297207
Sum429
Variance0.1671160683
MonotocityNot monotonic
2020-11-20T00:59:38.718981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
0362891.8%
 
12466.2%
 
2611.5%
 
3130.3%
 
440.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
0362891.8%
 
12466.2%
 
2611.5%
 
3130.3%
 
440.1%
 
ValueCountFrequency (%) 
61< 0.1%
 
440.1%
 
3130.3%
 
2611.5%
 
12466.2%
 

Inq Last 6Mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8555527448
Minimum0
Maximum8
Zeros1822
Zeros (%)46.1%
Memory size30.9 KiB
2020-11-20T00:59:38.843842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.997025005
Coefficient of variation (CV)1.165357731
Kurtosis2.163689287
Mean0.8555527448
Median Absolute Deviation (MAD)1
Skewness1.26526022
Sum3382
Variance0.9940588606
MonotocityNot monotonic
2020-11-20T00:59:38.970920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
0182246.1%
 
1124531.5%
 
258414.8%
 
32656.7%
 
4210.5%
 
5100.3%
 
630.1%
 
720.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
0182246.1%
 
1124531.5%
 
258414.8%
 
32656.7%
 
4210.5%
 
ValueCountFrequency (%) 
81< 0.1%
 
720.1%
 
630.1%
 
5100.3%
 
4210.5%
 

Pub Rec
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
0
3831 
1
 
120
2
 
2
ValueCountFrequency (%) 
0383196.9%
 
11203.0%
 
220.1%
 
2020-11-20T00:59:39.131282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-20T00:59:39.233897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:39.338175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Revol Bal
Real number (ℝ≥0)

ZEROS

Distinct3672
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14367.44751
Minimum0
Maximum140967
Zeros42
Zeros (%)1.1%
Memory size30.9 KiB
2020-11-20T00:59:39.481276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1240.4
Q16352
median11449
Q318151
95-th percentile35148.4
Maximum140967
Range140967
Interquartile range (IQR)11799

Descriptive statistics

Standard deviation13468.63453
Coefficient of variation (CV)0.937441012
Kurtosis18.01764983
Mean14367.44751
Median Absolute Deviation (MAD)5657
Skewness3.322035836
Sum56794520
Variance181404116.1
MonotocityNot monotonic
2020-11-20T00:59:39.643427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0421.1%
 
803230.1%
 
631430.1%
 
1484830.1%
 
1098030.1%
 
1133830.1%
 
1518330.1%
 
835730.1%
 
656530.1%
 
1303430.1%
 
Other values (3662)388498.3%
 
ValueCountFrequency (%) 
0421.1%
 
31< 0.1%
 
61< 0.1%
 
81< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
1409671< 0.1%
 
1319491< 0.1%
 
1309201< 0.1%
 
1247441< 0.1%
 
1234161< 0.1%
 

Total Paymnt
Real number (ℝ≥0)

Distinct3710
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14435.06432
Minimum0
Maximum58886.47343
Zeros2
Zeros (%)0.1%
Memory size30.9 KiB
2020-11-20T00:59:39.820480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2401.064047
Q16614.78722
median11907.35
Q319190.68001
95-th percentile35788.92425
Maximum58886.47343
Range58886.47343
Interquartile range (IQR)12575.89279

Descriptive statistics

Standard deviation10492.53033
Coefficient of variation (CV)0.7268779753
Kurtosis1.593830926
Mean14435.06432
Median Absolute Deviation (MAD)5937.176941
Skewness1.261678967
Sum57061809.25
Variance110093192.6
MonotocityNot monotonic
2020-11-20T00:59:39.983538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14288.7616980.2%
 
13148.1378670.2%
 
11907.3473270.2%
 
12029.4570.2%
 
11600.9860.2%
 
14288.7750.1%
 
11726.3250.1%
 
10956.7759650.1%
 
9011.55749450.1%
 
13263.9650.1%
 
Other values (3700)389398.5%
 
ValueCountFrequency (%) 
020.1%
 
91.391< 0.1%
 
151.81< 0.1%
 
165.371< 0.1%
 
203.551< 0.1%
 
ValueCountFrequency (%) 
58886.473431< 0.1%
 
58133.31991< 0.1%
 
58090.952071< 0.1%
 
58071.199821< 0.1%
 
58071.199771< 0.1%
 

Interactions

2020-11-20T00:59:14.291376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:14.504588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:14.660454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:14.819208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:14.971545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:15.119724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:15.267529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:15.416222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:15.570586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:15.726637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:15.883253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.040758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.198511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.356192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.509051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.657818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.805966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:16.955322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:17.112923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:17.272928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:17.425046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:17.581560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:17.742537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:17.901551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:18.054858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:18.205570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:18.357731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:18.633152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:18.800076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:18.965205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:19.123379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:19.275685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:19.429369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:19.586746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:19.740092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:19.885661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.034118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.181656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.336725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.495400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.645980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.794117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:20.944934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.095106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.242064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.385887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.527871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.670099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.820311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:21.969486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:22.118871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:22.270967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:22.426393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:22.580007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:22.725734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:22.870079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:23.013549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:23.164731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:23.316551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:23.467441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:23.615506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:23.767239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.064402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.224413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.373098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.521866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.672762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.817413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:24.969919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:25.123899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:25.273119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:25.435217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:25.598499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:25.760308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:25.933282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:26.089240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:26.243071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:26.402143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:26.565296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:26.727725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:26.886611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.049339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.210379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.371974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.526922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.678433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.831018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:27.984952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:28.144199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:28.319317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:28.471148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:28.621468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:28.771205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:28.921108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:29.067169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:29.221549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:29.364891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:29.509695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:29.661206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:29.859748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-11-20T00:59:40.147907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-20T00:59:40.396736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-20T00:59:40.643178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-20T00:59:40.911697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-20T00:59:41.204878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-20T00:59:30.233002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:31.110817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:31.402079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-20T00:59:31.569636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

NameEmail IDGenderDt_AppliedUniversityLoan AmntFunded amnt invTERMInt RateINSTALLMENTGRADESub GradeHome OwnershipAnnual IncVerification StatusLoan WriteoffPURPOSEZip CodeAdd StateDTIDelinq 2YrsInq Last 6MthsPub RecRevol BalTotal Paymnt
0Calley Gironcgiron0@ehow.comFemale01/01/81Warner Southern College50004975.036 months0.107162.87BB2RENT24000.0Verified0credit_card860xxAZ27.65010136485863.155187
1Linus Studlstud1@washington.eduMale02/01/81Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha25002500.060 months0.15359.83CC4RENT30000.0Source Verified1car309xxGA1.0005016871014.530000
2Lorelle Ambagelambage2@wix.comFemale03/01/81Technische Universität Bergakademie Freiberg24002400.036 months0.16084.33CC5RENT12252.0Not Verified0small_business606xxIL8.7202029563005.666844
3Anna-diane Larratalarrat3@economist.comFemale04/01/81Divine Word College of Legazpi1000010000.036 months0.135339.31CC1RENT49200.0Source Verified0other917xxCA20.00010559812231.890000
4Gill RuskeNaNFemale05/01/81East China Jiao Tong University30003000.060 months0.12767.79BB5RENT80000.0Source Verified0other972xxOR17.94000277834066.908161
5Evelyn MacFaulemacfaul5@theatlantic.comFemale06/01/81Ahmedabad University50005000.036 months0.079156.46AA4RENT36000.0Source Verified0wedding852xxAZ11.2003079635632.210000
6Ainslie Rainardarainard6@virginia.eduFemale07/01/81NaN70007000.060 months0.160170.08CC5RENT47004.0Not Verified0debt_consolidation280xxNC23.510101772610137.840010
7Emmott Hambyehamby7@prnewswire.comMale08/01/81Institute of Business Management30003000.036 months0.186109.43EE1RENT48000.0Source Verified0car900xxCA5.3502082213939.135294
8Shem Toomerstoomer8@home.plMale09/01/81Osaka University of Education56005600.060 months0.213152.39FF2OWN40000.0Source Verified1small_business958xxCA5.550205210647.500000
9Giana Aberhartgaberhart9@mozilla.comFemale10/01/81American Public University53755350.060 months0.127121.45BB5RENT15000.0Verified1other774xxTX18.0800092791484.590000

Last rows

NameEmail IDGenderDt_AppliedUniversityLoan AmntFunded amnt invTERMInt RateINSTALLMENTGRADESub GradeHome OwnershipAnnual IncVerification StatusLoan WriteoffPURPOSEZip CodeAdd StateDTIDelinq 2YrsInq Last 6MthsPub RecRevol BalTotal Paymnt
3943Merla Thebemthebeq7@cocolog-nifty.comFemale21/10/91North Eastern Hill University60006000.036 months0.163211.81DD1RENT39564.0Verified1debt_consolidation606xxIL23.7821020283388.960000
3944Marcellina Dinnegesmdinnegesq8@infoseek.co.jpFemale22/10/91Universidade Católica de Santos24002400.036 months0.11779.39BB3RENT39800.0Not Verified0other303xxGA14.32000154972836.660516
3945Way Symondswsymondsq9@mlb.comMale23/10/91American International University West Africa2500025000.060 months0.183638.25DD5MORTGAGE156000.0Source Verified0house944xxCA5.850001070937936.750000
3946Ailene MatejkaNaNFemale24/10/91Kaya University2000020000.036 months0.117661.52BB3RENT80700.0Verified0debt_consolidation946xxCA13.67010721123406.523000
3947Samuel OverelNaNMale25/10/91Northwestern University1200012000.060 months0.183306.36DD5MORTGAGE34000.0Not Verified1debt_consolidation177xxPA12.5600061149667.950000
3948Corbie Creeboeccreeboeqc@sitemeter.comMale26/10/91Shaheed Rajaei Teacher Training University1200012000.036 months0.135407.17CC1RENT125000.0Source Verified0wedding086xxNJ13.180104628614657.917650
3949Bobbe Ochterloniebochterlonieqd@ezinearticles.comFemale27/10/91Dhofar University1500015000.036 months0.124501.23BB4RENT72000.0Verified0debt_consolidation104xxNY7.470101214716729.253640
3950Corella Espositocespositoqe@macromedia.comFemale28/10/91University of Jan Evangelista Purkyne1200012000.036 months0.060365.23AA1OWN48000.0Not Verified0debt_consolidation365xxAL23.350002238513148.137860
3951Prince Dibdinpdibdinqf@businessinsider.comMale29/10/91College in Sládkovičovo1500015000.060 months0.160364.46CC5RENT50000.0Verified1debt_consolidation907xxCA18.26010979910883.540000
3952Georgette Warrattgwarrattqg@java.comFemale30/10/91Technical University of Lublin1500014975.060 months0.153358.98CC4MORTGAGE32976.0Not Verified1debt_consolidation177xxPA17.90010795611704.260000